Purpose: To examine the social network factors associated with changes in nutrition risk scores, measured by SCREEN-8, over three years, in community-dwelling Canadians aged 45 years and older, using data from the Canadian Longitudinal Study on Aging (CLSA).Methods: Change in SCREEN-8 scores between the baseline and first follow-up waves of the CLSA was calculated by subtracting SCREEN-8 scores at follow-up from baseline scores. Multivariable linear regression was used to examine the factors associated with change in SCREEN-8 score.Results: The mean SCREEN-8 score at baseline was 38.7 (SD = 6.4), and the mean SCREEN-8 score at follow-up was 37.9 (SD = 6.6). The mean change in SCREEN-8 score was -0.90 (SD = 5.99). Higher levels of social participation (participation in community activities) were associated with increases in SCREEN-8 scores between baseline and follow-up, three years later.Conclusions: Dietitians should be aware that individuals with low levels of social participation may be at risk for having their nutritional status decrease over time and consideration should be given to screening them proactively for nutrition risk. Dietitians can develop and support programs aimed at combining food with social participation.

BACKGROUND: In high-income countries (HICs), between 65% and 70% of community-dwelling adults aged 65 and older are at high nutrition risk. Nutrition risk is the risk of poor dietary intake and nutritional status. Consequences of high nutrition risk include frailty, hospitalization, death, and reduced quality of life. Social factors (such as social support and commensality) are known to influence eating behavior in later life; however, to the authors\' knowledge, no reviews have been conducted examining how these social factors are associated with nutrition risk specifically.
OBJECTIVE: The objective of this scoping review is to understand the extent and type of evidence concerning the relationship between social factors and nutrition risk among community-dwelling older adults in HICs and to identify social interventions that address nutrition risk in community-dwelling older adults in HICs.
METHODS: This review will follow the scoping review methodology as outlined by the JBI Manual for Evidence Synthesis and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search will include MEDLINE (Ovid), CINAHL, PsycINFO, and Web of Science. There will be no date limits placed on the search. However, only resources available in English will be included. EndNote (Clarivate Analytics) and Covidence (Veritas Health Innovation Ltd) will be used for reference management and removal of duplicate studies. Articles will be screened, and data will be extracted by at least 2 independent reviewers using Covidence. Data to be extracted will include study characteristics (country, methods, aims, design, and dates), participant characteristics (population description, inclusion and exclusion criteria, recruitment method, total number of participants, and demographics), how nutrition risk was measured (including the tool used to measure nutrition risk), social factors or interventions examined (including how these were measured or determined), the relationship between nutrition risk and the social factors examined, and the details of social interventions designed to address nutrition risk.
RESULTS: The scoping review was started in October 2023 and will be finalized by August 2024. The findings will describe the social factors commonly examined in the nutrition risk literature, the relationship between these social factors and nutrition risk, the social factors that have an impact on nutrition risk, and social interventions designed to address nutrition risk. The results of the extracted data will be presented in the form of a narrative summary with accompanying tables.
CONCLUSIONS: Given the high prevalence of nutrition risk in community-dwelling older adults in HICs and the negative consequences of nutrition risk, it is essential to understand the social factors associated with nutrition risk. The results of the review are anticipated to aid in identifying individuals who should be screened proactively for nutrition risk and inform programs, policies, and interventions designed to reduce the prevalence of nutrition risk.
UNASSIGNED: DERR1-10.2196/56714.

OBJECTIVE: Critical illness induces hypermetabolism and hypercatabolism, increasing nutrition risk (NR). Early NR identification is crucial for improving outcomes. We assessed four nutrition screening tools (NSTs) complementarity with the Global Leadership Initiative on Malnutrition (GLIM) criteria in critically ill patients.
METHODS: We conducted a comparative study using data from a cohort involving five intensive care units (ICUs), screening patients for NR using NRS-2002 and modified-NUTRIC tools, with three cutoffs (≥3, ≥4, ≥5), and malnutrition diagnosed by GLIM criteria. Our outcomes of interest included ICU and in-hospital mortality, ICU and hospital length of stay (LOS), and ICU readmission. We examined accuracy metrics and complementarity between NSTs and GLIM criteria about clinical outcomes through logistic regression and Cox regression. We established a four-category independent variable: NR(-)/GLIM(-) as the reference, NR(-)/GLIM(+), NR(+)/GLIM(-), and NR(+)/GLIM(+).
RESULTS: Of the 377 patients analyzed (median age 64 years [interquartile range: 54-71] and 53.8% male), NR prevalence varied from 87% to 40.6%, whereas 64% presented malnutrition (GLIM criteria). NRS-2002 (score ≥4) showed superior accuracy for GLIM-based malnutrition. Multivariate analysis revealed mNUTRIC(+)/GLIM(+) increased >2 times in the likelihood of ICU and in-hospital mortality, ICU and hospital LOS, and ICU readmission compared with the reference group.
CONCLUSIONS: No NST exhibited satisfactory complementarity to the GLIM criteria in our study, emphasizing the necessity for comprehensive nutrition assessment for all patients, irrespective of NR status. We recommend using mNUTRIC if the ICU team opts for nutrition screening, as it demonstrated superior prognostic value compared with NRS-2002, and applying GLIM criteria in all patients.

BACKGROUND: The emergency department (ED) is the most frequent access route to the hospital. Nutrition risk (NR) screening allows the early identification of patients at risk of malnutrition. This study aimed to evaluate the feasibility and predictive validity of five different tools in EDs: Nutritional Risk Screening 2002 (NRS-2002), Nutritional Risk Emergency 2017 (NRE-2017), Royal Free Hospital-Nutritional Prioritizing Tool (RFH-NPT), Malnutrition Universal Screening (MUST), and Malnutrition Screening Tool (MST).
METHODS: Patients with scores ≥3 according to the NRS-2002, ≥1.5 according to the NRE-2017, and ≥2 according to the MUST, RFH-NPT, or MST were classified with NR. Prolonged length of stay (LOS) and 1-year mortality were evaluated.
RESULTS: 431 patients were evaluated (57.31 ± 15.6 years of age; 54.4% women) in a public hospital in southern Brazil. The prevalence of NR was: 35% according to the NRS-2002, 43% according to the MST, 45% according to the NRE-2017 and MUST, and 49% according to the RFH-NPT. Patients with NR, had a greater risk of prolonged LOS (P < 0.001). The presence of NR was associated with an increased risk of 1-year mortality according to the NRS-2002 (hazard ratio [HR]: 4.04; 95% CI, 2.513-6.503), MST (HR: 2.60; 95% CI, 1.701-3.996), NRE-2017 (HR: 4.82; 95% CI, 2.753-8.443), MUST (HR: 4.00; 95% CI, 2.385-6.710), and RFH-NPT (HR: 5.43; 95% CI, 2.984-9.907).
CONCLUSIONS: NRE-2017 does not require objective data and presented predictive validity for all outcomes assessed, regardless of the severity of the disease, and thus appears to be the most appropriate tool for carrying out NR screening in the ED.

BACKGROUND: Nutrition risk is prevalent in intensive care unit (ICU) settings and related to poor prognoses. We aimed to evaluate the concurrent and predictive validity of different nutrition risk screening tools in the ICU.
METHODS: Data were collected between 2019 and 2022 in six ICUs (n = 450). Nutrition risk was evaluated by modified Nutrition Risk in Critically ill (mNUTRIC), Nutritional Risk Screening (NRS-2002), Malnutrition Screening Tool (MST), Malnutrition Universal Screening Tool (MUST), and Nutritional Risk in Emergency (NRE-2017). Accuracy and agreement of the tools were assessed; logistic regression was used to verify the association between nutrition risk and prolonged ICU stay; Cox regression was used for mortality in the ICU, both with adjustment for confounders.
RESULTS: NRS-2002 ≥5 showed the best accuracy (0.63 [95% CI, 0.58-0.69]) with mNUTRIC, and MST with NRS-2002 ≥5 (0.76 [95% CI, 0.71-0.80]). All tools had a poor/fair agreement with mNUTRIC (k = 0.019-0.268) and moderate agreement with NRS-2002 ≥5 (k = 0.474-0.503). MUST (2.26 [95% CI 1.40-3.63]) and MST (1.69 [95% CI, 1.09-2.60]) predicted death in the ICU, and the NRS-2002 ≥5 (1.56 [95% CI 1.02-2.40]) and mNUTRIC (1.86 [95% CI, 1.26-2.76]) predicted prolonged ICU stay.
CONCLUSIONS: No nutrition risk screening tool demonstrated a satisfactory concurrent validity; only the MUST and MST predicted ICU mortality and the NRS-2002 ≥5 and mNUTRIC predicted prolonged ICU stay, suggesting that it could be appropriate to adopt the ESPEN recommendation to assess nutrition status in patients with ≥48 h in the ICU.

BACKGROUND: Children with underweight in the first 2 years have lower body mass index z-score (zBMI) and height-for-age z-score (HAZ) in later childhood. It is not known if underweight in the first 2 years is associated with nutrition risk in later childhood.
OBJECTIVE: (1) Determine the relationship between underweight (zBMI < -2) in the first 2 years and nutrition risk measured by the Nutrition Screening for Toddlers and Preschoolers (NutriSTEP) score from 18 months to 5 years. (2) Explore the relationship between underweight in the first 2 years and the NutriSTEP subscores for eating behaviours and dietary intake from 18 months to 5 years.
METHODS: This was a prospective study, including healthy full-term children in Canada aged 0-5 years. zBMI was calculated using measured heights and weights and the WHO growth standards. NutriSTEP score was measured using a parent-completed survey and ranged from 0 to 68. Nutrition risk was defined as a score ≥21. Linear mixed effects models were used.
RESULTS: Four thousand nine hundred twenty-nine children were included in this study. At enrolment, 51.9% of participants were male. The prevalence of underweight children was 8.8%. Underweight in the first 2 years was associated with higher NutriSTEP (0.79, 95% CI: 0.29,1.29), higher eating behaviour subscore (0.24, 95% CI: 0.03, 0.46) at 3 years and higher odds of nutrition risk (OR: 1.39, 95% CI: 1.07,1.82) at 5 years.
CONCLUSIONS: Children with underweight in the first 2 years had higher nutrition risk in later childhood. Further research is needed to understand the factors which influence these relationships.

Determine if nutrition risk, as measured by SCREEN-8 is predictive of 3-year strength and performance indicators among community-living older adults after adjusting for key demographic and health variables. Sex-stratified analyses were also determined.
Cohort study with baseline and 3-year follow-up data from the Canadian Longitudinal Study on Aging (CLSA).
Participants 55 years and older at baseline were included (n = 22,502); those who reported nasogastric or abdominal tube feeding at either timepoint were excluded (n = 26). The final sample of participants available for analyses slightly varied depending on completion of the three outcome variables. List-wise deletion was used for nutrition risk and covariates to arrive at the sample available for analysis (n = 17,250).
The valid and reliable SCREEN-8 tool was used to measure nutrition risk. The minimum and maximum score of SCREEN-8 is 0 and 48, respectively, with lower scores indicating greater nutrition risk. Baseline SCREEN-8 score was used in analyses. Grip strength, chair rise test time and gait speed assessed at the 3-year follow-up were the strength and performance outcomes. Criteria outlined by the European Working Group on Sarcopenia in Older People 2 were used to determine low performance for grip strength (<27 kg for males and <16 kg for females), chair rise test time (>15 seconds) and gait speed (≤0.8 m/s).
Half of participants were female (49.4%) and mean age was 66.7 years (SD 7.9). Mean SCREEN-8 score was 39.2 (SD 6.0). Low grip strength, chair rise test performance and gait speed were found in 18.5%, 19.6% and 29.3% of participants, respectively. After adjusting for covariates (e.g., sex, age, education), SCREEN-8 score was significantly associated with grip strength (F = 11.21, p = .001; OR = 0.98, CI [0.97, 0.99]), chair rise time (F = 5.97, p = .015; OR = 0.99, CI [0.97, 0.997]), and gait speed (F = 9.99, p = .002; OR = 0.98, CI [0.97, 0.99]). Similar interpretation was seen in sex-stratified analyses, although chair rise time was not significant. Age, body mass index, Life Space Index Score and self-rated health were consistently associated with all outcome measures.
Nutrition risk, as measured by SCREEN-8, significantly predicted 3-year strength and performance measures. Greater nutrition risk is associated with an increased odds of low performance on grip strength, chair rise test, and gait speed. Future research should implement nutrition risk screening in primary care settings with subsequent assessment and treatment for at risk clients to determine if nutrition interventions implemented post screening can delay age-related losses in strength and performance.

For patients who are critically ill, the recommended nutrition risk screening tools are the Nutrition Risk in the Critically Ill (NUTRIC) and the Nutritional Risk Screening 2002 (NRS-2002) have limitations.
To develop a new screening tool, the Screening of Nutritional Risk in Intensive Care (SCREENIC score), and assess its predictive validity.
A secondary analysis of a prospective cohort study was conducted. Variables from several nutritional screening and assessment tools were considered. The high nutrition risk cutoff point was defined using mNUTRIC as a reference. Predictive validity was evaluated using logistic regression and Cox regression.
The study included 450 patients (64 [54-71] years, 52.2% men). The SCREENIC score comprised six questions: (1) does the patient have ≥2 comorbidities (1.3 points); (2) was the patient hospitalized for ≥2 days before intensive care unit (ICU) admission (0.9 points); (3) does the patient have sepsis (1.0 point); (4) was the patient on mechanical ventilation upon ICU admission (1.2 points); (5) is the patient aged >65 years (1.2 points); and (6) does the patient exhibit signs of moderate/severe muscle mass loss according to the physical exam (0.6 points). The high nutrition risk cutoff point was set at 4.0. SCREENIC demonstrated moderate agreement (κ = 0.564) and high accuracy (0.896 [95% CI, 0.867-0.925]) with mNUTRIC. It predicted prolonged ICU (odds ratio [OR] = 1.81 [95% CI, 1.14-2.85]) and hospital stay (OR = 2.15 [95% CI, 1.37-3.38]).
The SCREENIC score comprises questions with variables that do not require nutrition history. Further evaluation of its applicability, reproducibility, and validity in guiding nutrition therapy is needed using large external cohorts.

This study aimed to determine which social network, demographic, and health-indicator variables were able to predict the development of high nutrition risk in Canadian adults at midlife and beyond, using data from the Canadian Longitudinal Study on Aging. Multivariable binomial logistic regression was used to examine the predictors of the development of high nutrition risk at follow-up, 3 years after baseline. At baseline, 35.0 per cent of participants were at high nutrition risk and 42.2 per cent were at high risk at follow-up. Lower levels of social support, lower social participation, depression, and poor self-rated healthy aging were associated with the development of high nutrition risk at follow-up. Individuals showing these factors should be screened proactively for nutrition risk.

Malnutrition is correlated with poor cognition; however, an understanding of the association between nutrition risk, which precedes malnutrition, and cognition is lacking. This study aimed to determine if nutrition risk measured with the SCREEN-8 tool is associated with cognitive performance among cognitively healthy adults aged 55+, after adjusting for demographic and lifestyle covariates. Sex- and age-stratified analyses were also explored. Baseline data from the Canadian Longitudinal Study on Aging was used. Cognition was determined using a 6-measure composite score based on four executive functions and two memory tasks, taking into account age, sex, and education. Multivariable linear regression was performed while adjusting for body mass index (BMI), lifestyle, and health covariates in the entire sample (n = 11 378) and then stratified by sex and age. Approximately half of participants were female (54.5%) aged 65+ (54.1%). Greater nutrition risk was associated with poorer cognitive performance in the entire sample (F[1, 11 368] = 5.36, p = 0.021) and among participants aged 55-64 (n = 5227; F[1, 5217] = 5.45, p = 0.020). Sex differences in lifestyle and health factors associated with cognition were apparent, but nutrition risk was not associated with cognition in sex-stratified models. Based on this analysis, there may be an association between nutrition risk and cognitive performance in older adults. When screening for either cognitive impairment or nutrition risk, complementary assessments for these conditions is warranted, as early intervention may provide benefit.